In a research field, population and sample have both similarities and differences. The two are similar since they refer to a group of people or objects under analysis. However, the difference relates to the size because a population indicates all people or items that have a specific feature, while a sample is a randomly selected part of the population. Researchers usually use some terms to describe the two phenomena. A parameter presents a characteristic feature of the entire population, while statistics, either descriptive or inferential, refers to a sample. According to Singh (2018), descriptive statistics presents some sample properties, while an inferential approach analyzes the sample to make conclusions about the broader population. One can say that there is a significant difference between these statistics kinds.
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Variables are essential phenomena in every research, and there can be continuous and categorical types. Continuous variables provide questioned people with an opportunity to choose a value of some measurement scales. Common examples of these variables include income, temperature, and weight. Simultaneously, categorical variables denote that people should choose among the offered categories. Their possible examples include ethnicity, education level, and employment type. In addition to that, variables can be independent and dependent. As for the given scenario, two different treatments that a counselor is using with depressed clients are independent variables, while measures of depression in the clients are dependent ones.
Finally, the frequency distribution is a phenomenon that shows how often a selected variable is found among the questioned people. For example, if a sample of ten individuals consists of four Americans, three African Americans, and three Hispanics, a simple frequency distribution will be four for Americans, three for African Americans, and three for Hispanics. Thus, this phenomenon is useful in understanding the sample characteristic features.
Singh, S. (2018). Statistics: Descriptive and inferential. Towards Data Science. Web.